analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import csv\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accidents = pd.read_csv(\"accident_cleaned_data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vechiles = pd.read_csv(\"Vechicles_cleaned_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casu= pd.read_csv(\"casualties_cleaned_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick view on Accidents\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Accident_Index                0\n",
       "Longitude                     0\n",
       "Latitude                      0\n",
       "Accident_Severity             0\n",
       "Number_of_Vehicles            0\n",
       "Number_of_Casualties          0\n",
       "Date                          0\n",
       "Day_of_Week                   0\n",
       "Time                          0\n",
       "Local_Authority_(District)    0\n",
       "Local_Authority_(Highway)     0\n",
       "Speed_limit                   0\n",
       "Light_Conditions              0\n",
       "Weather_Conditions            0\n",
       "Road_Surface_Conditions       0\n",
       "Special_Conditions_at_Site    0\n",
       "Urban_or_Rural_Area           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accidents.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df_accidents.duplicated().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 898555 entries, 0 to 898554\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   Accident_Index              898555 non-null  object \n",
      " 1   Longitude                   898555 non-null  float64\n",
      " 2   Latitude                    898555 non-null  float64\n",
      " 3   Accident_Severity           898555 non-null  int64  \n",
      " 4   Number_of_Vehicles          898555 non-null  int64  \n",
      " 5   Number_of_Casualties        898555 non-null  int64  \n",
      " 6   Date                        898555 non-null  object \n",
      " 7   Day_of_Week                 898555 non-null  int64  \n",
      " 8   Time                        898555 non-null  object \n",
      " 9   Local_Authority_(District)  898555 non-null  int64  \n",
      " 10  Local_Authority_(Highway)   898555 non-null  object \n",
      " 11  Speed_limit                 898555 non-null  int64  \n",
      " 12  Light_Conditions            898555 non-null  int64  \n",
      " 13  Weather_Conditions          898555 non-null  int64  \n",
      " 14  Road_Surface_Conditions     898555 non-null  int64  \n",
      " 15  Special_Conditions_at_Site  898555 non-null  int64  \n",
      " 16  Urban_or_Rural_Area         898555 non-null  int64  \n",
      "dtypes: float64(2), int64(11), object(4)\n",
      "memory usage: 116.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_accidents.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(898555, 17)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accidents.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>Time</th>\n",
       "      <th>Local_Authority_(District)</th>\n",
       "      <th>Local_Authority_(Highway)</th>\n",
       "      <th>Speed_limit</th>\n",
       "      <th>Light_Conditions</th>\n",
       "      <th>Weather_Conditions</th>\n",
       "      <th>Road_Surface_Conditions</th>\n",
       "      <th>Special_Conditions_at_Site</th>\n",
       "      <th>Urban_or_Rural_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>898555</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "      <td>898555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>898555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2014984139614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-10-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E10000016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.393471</td>\n",
       "      <td>52.557925</td>\n",
       "      <td>2.838157</td>\n",
       "      <td>1.826297</td>\n",
       "      <td>1.343093</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.115010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.141962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.631375</td>\n",
       "      <td>1.936235</td>\n",
       "      <td>1.551820</td>\n",
       "      <td>1.372541</td>\n",
       "      <td>0.104764</td>\n",
       "      <td>1.349956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.402066</td>\n",
       "      <td>1.451622</td>\n",
       "      <td>0.398710</td>\n",
       "      <td>0.710371</td>\n",
       "      <td>0.827161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.919744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.007326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.959299</td>\n",
       "      <td>1.641881</td>\n",
       "      <td>1.598791</td>\n",
       "      <td>0.649301</td>\n",
       "      <td>0.714385</td>\n",
       "      <td>0.476956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.516225</td>\n",
       "      <td>49.912941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.312257</td>\n",
       "      <td>51.483382</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.335049</td>\n",
       "      <td>52.211135</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.197891</td>\n",
       "      <td>53.453312</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.759398</td>\n",
       "      <td>60.662043</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Accident_Index      Longitude       Latitude  Accident_Severity  \\\n",
       "count          898555  898555.000000  898555.000000      898555.000000   \n",
       "unique         898555            NaN            NaN                NaN   \n",
       "top     2014984139614            NaN            NaN                NaN   \n",
       "freq                1            NaN            NaN                NaN   \n",
       "mean              NaN      -1.393471      52.557925           2.838157   \n",
       "std               NaN       1.402066       1.451622           0.398710   \n",
       "min               NaN      -7.516225      49.912941           1.000000   \n",
       "25%               NaN      -2.312257      51.483382           3.000000   \n",
       "50%               NaN      -1.335049      52.211135           3.000000   \n",
       "75%               NaN      -0.197891      53.453312           3.000000   \n",
       "max               NaN       1.759398      60.662043           3.000000   \n",
       "\n",
       "        Number_of_Vehicles  Number_of_Casualties        Date    Day_of_Week  \\\n",
       "count        898555.000000         898555.000000      898555  898555.000000   \n",
       "unique                 NaN                   NaN        2191            NaN   \n",
       "top                    NaN                   NaN  2010-10-01            NaN   \n",
       "freq                   NaN                   NaN         698            NaN   \n",
       "mean              1.826297              1.343093         NaN       4.115010   \n",
       "std               0.710371              0.827161         NaN       1.919744   \n",
       "min               1.000000              1.000000         NaN       1.000000   \n",
       "25%               1.000000              1.000000         NaN       2.000000   \n",
       "50%               2.000000              1.000000         NaN       4.000000   \n",
       "75%               2.000000              1.000000         NaN       6.000000   \n",
       "max              67.000000             93.000000         NaN       7.000000   \n",
       "\n",
       "            Time  Local_Authority_(District) Local_Authority_(Highway)  \\\n",
       "count     898555               898555.000000                    898555   \n",
       "unique      1439                         NaN                       207   \n",
       "top     17:00:00                         NaN                 E10000016   \n",
       "freq        8889                         NaN                     25989   \n",
       "mean         NaN                  350.141962                       NaN   \n",
       "std          NaN                  260.007326                       NaN   \n",
       "min          NaN                    1.000000                       NaN   \n",
       "25%          NaN                  112.000000                       NaN   \n",
       "50%          NaN                  325.000000                       NaN   \n",
       "75%          NaN                  530.000000                       NaN   \n",
       "max          NaN                  941.000000                       NaN   \n",
       "\n",
       "          Speed_limit  Light_Conditions  Weather_Conditions  \\\n",
       "count   898555.000000     898555.000000       898555.000000   \n",
       "unique            NaN               NaN                 NaN   \n",
       "top               NaN               NaN                 NaN   \n",
       "freq              NaN               NaN                 NaN   \n",
       "mean        38.631375          1.936235            1.551820   \n",
       "std         13.959299          1.641881            1.598791   \n",
       "min         10.000000          1.000000            1.000000   \n",
       "25%         30.000000          1.000000            1.000000   \n",
       "50%         30.000000          1.000000            1.000000   \n",
       "75%         50.000000          4.000000            1.000000   \n",
       "max         70.000000          7.000000            9.000000   \n",
       "\n",
       "        Road_Surface_Conditions  Special_Conditions_at_Site  \\\n",
       "count             898555.000000               898555.000000   \n",
       "unique                      NaN                         NaN   \n",
       "top                         NaN                         NaN   \n",
       "freq                        NaN                         NaN   \n",
       "mean                   1.372541                    0.104764   \n",
       "std                    0.649301                    0.714385   \n",
       "min                    1.000000                    0.000000   \n",
       "25%                    1.000000                    0.000000   \n",
       "50%                    1.000000                    0.000000   \n",
       "75%                    2.000000                    0.000000   \n",
       "max                    5.000000                    7.000000   \n",
       "\n",
       "        Urban_or_Rural_Area  \n",
       "count         898555.000000  \n",
       "unique                  NaN  \n",
       "top                     NaN  \n",
       "freq                    NaN  \n",
       "mean               1.349956  \n",
       "std                0.476956  \n",
       "min                1.000000  \n",
       "25%                1.000000  \n",
       "50%                1.000000  \n",
       "75%                2.000000  \n",
       "max                2.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accidents.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>Local_Authority_(District)</th>\n",
       "      <th>Speed_limit</th>\n",
       "      <th>Light_Conditions</th>\n",
       "      <th>Weather_Conditions</th>\n",
       "      <th>Road_Surface_Conditions</th>\n",
       "      <th>Special_Conditions_at_Site</th>\n",
       "      <th>Urban_or_Rural_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.437628</td>\n",
       "      <td>0.025402</td>\n",
       "      <td>0.015126</td>\n",
       "      <td>-0.040334</td>\n",
       "      <td>-0.000378</td>\n",
       "      <td>-0.404787</td>\n",
       "      <td>-0.061076</td>\n",
       "      <td>0.008590</td>\n",
       "      <td>-0.027861</td>\n",
       "      <td>-0.067232</td>\n",
       "      <td>-0.015001</td>\n",
       "      <td>-0.100809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>-0.437628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032664</td>\n",
       "      <td>-0.038574</td>\n",
       "      <td>0.029588</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>0.141626</td>\n",
       "      <td>0.062134</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>0.027190</td>\n",
       "      <td>0.085564</td>\n",
       "      <td>0.014238</td>\n",
       "      <td>0.058659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accident_Severity</th>\n",
       "      <td>0.025402</td>\n",
       "      <td>-0.032664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076186</td>\n",
       "      <td>-0.068747</td>\n",
       "      <td>0.003336</td>\n",
       "      <td>-0.044134</td>\n",
       "      <td>-0.079701</td>\n",
       "      <td>-0.054637</td>\n",
       "      <td>0.021082</td>\n",
       "      <td>0.013631</td>\n",
       "      <td>-0.001281</td>\n",
       "      <td>-0.083930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <td>0.015126</td>\n",
       "      <td>-0.038574</td>\n",
       "      <td>0.076186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245430</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>-0.006458</td>\n",
       "      <td>0.082694</td>\n",
       "      <td>-0.082485</td>\n",
       "      <td>-0.023843</td>\n",
       "      <td>-0.050638</td>\n",
       "      <td>-0.014334</td>\n",
       "      <td>0.039475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <td>-0.040334</td>\n",
       "      <td>0.029588</td>\n",
       "      <td>-0.068747</td>\n",
       "      <td>0.245430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.016901</td>\n",
       "      <td>0.139023</td>\n",
       "      <td>0.030567</td>\n",
       "      <td>-0.002428</td>\n",
       "      <td>0.022130</td>\n",
       "      <td>0.010883</td>\n",
       "      <td>0.113402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_of_Week</th>\n",
       "      <td>-0.000378</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>0.003336</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000881</td>\n",
       "      <td>-0.016014</td>\n",
       "      <td>0.007572</td>\n",
       "      <td>-0.001683</td>\n",
       "      <td>-0.009044</td>\n",
       "      <td>-0.002755</td>\n",
       "      <td>-0.017346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local_Authority_(District)</th>\n",
       "      <td>-0.404787</td>\n",
       "      <td>0.141626</td>\n",
       "      <td>-0.044134</td>\n",
       "      <td>-0.006458</td>\n",
       "      <td>0.016901</td>\n",
       "      <td>-0.000881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205138</td>\n",
       "      <td>0.011777</td>\n",
       "      <td>0.048413</td>\n",
       "      <td>0.081917</td>\n",
       "      <td>0.025646</td>\n",
       "      <td>0.257345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speed_limit</th>\n",
       "      <td>-0.061076</td>\n",
       "      <td>0.062134</td>\n",
       "      <td>-0.079701</td>\n",
       "      <td>0.082694</td>\n",
       "      <td>0.139023</td>\n",
       "      <td>-0.016014</td>\n",
       "      <td>0.205138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.093459</td>\n",
       "      <td>0.036517</td>\n",
       "      <td>0.132536</td>\n",
       "      <td>0.071674</td>\n",
       "      <td>0.682524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light_Conditions</th>\n",
       "      <td>0.008590</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>-0.054637</td>\n",
       "      <td>-0.082485</td>\n",
       "      <td>0.030567</td>\n",
       "      <td>0.007572</td>\n",
       "      <td>0.011777</td>\n",
       "      <td>0.093459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115586</td>\n",
       "      <td>0.191809</td>\n",
       "      <td>0.005631</td>\n",
       "      <td>0.072806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather_Conditions</th>\n",
       "      <td>-0.027861</td>\n",
       "      <td>0.027190</td>\n",
       "      <td>0.021082</td>\n",
       "      <td>-0.023843</td>\n",
       "      <td>-0.002428</td>\n",
       "      <td>-0.001683</td>\n",
       "      <td>0.048413</td>\n",
       "      <td>0.036517</td>\n",
       "      <td>0.115586</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.315677</td>\n",
       "      <td>0.018757</td>\n",
       "      <td>0.039091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Road_Surface_Conditions</th>\n",
       "      <td>-0.067232</td>\n",
       "      <td>0.085564</td>\n",
       "      <td>0.013631</td>\n",
       "      <td>-0.050638</td>\n",
       "      <td>0.022130</td>\n",
       "      <td>-0.009044</td>\n",
       "      <td>0.081917</td>\n",
       "      <td>0.132536</td>\n",
       "      <td>0.191809</td>\n",
       "      <td>0.315677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046739</td>\n",
       "      <td>0.134827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Special_Conditions_at_Site</th>\n",
       "      <td>-0.015001</td>\n",
       "      <td>0.014238</td>\n",
       "      <td>-0.001281</td>\n",
       "      <td>-0.014334</td>\n",
       "      <td>0.010883</td>\n",
       "      <td>-0.002755</td>\n",
       "      <td>0.025646</td>\n",
       "      <td>0.071674</td>\n",
       "      <td>0.005631</td>\n",
       "      <td>0.018757</td>\n",
       "      <td>0.046739</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban_or_Rural_Area</th>\n",
       "      <td>-0.100809</td>\n",
       "      <td>0.058659</td>\n",
       "      <td>-0.083930</td>\n",
       "      <td>0.039475</td>\n",
       "      <td>0.113402</td>\n",
       "      <td>-0.017346</td>\n",
       "      <td>0.257345</td>\n",
       "      <td>0.682524</td>\n",
       "      <td>0.072806</td>\n",
       "      <td>0.039091</td>\n",
       "      <td>0.134827</td>\n",
       "      <td>0.073730</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Longitude  Latitude  Accident_Severity  \\\n",
       "Longitude                    1.000000 -0.437628           0.025402   \n",
       "Latitude                    -0.437628  1.000000          -0.032664   \n",
       "Accident_Severity            0.025402 -0.032664           1.000000   \n",
       "Number_of_Vehicles           0.015126 -0.038574           0.076186   \n",
       "Number_of_Casualties        -0.040334  0.029588          -0.068747   \n",
       "Day_of_Week                 -0.000378  0.003977           0.003336   \n",
       "Local_Authority_(District)  -0.404787  0.141626          -0.044134   \n",
       "Speed_limit                 -0.061076  0.062134          -0.079701   \n",
       "Light_Conditions             0.008590  0.002957          -0.054637   \n",
       "Weather_Conditions          -0.027861  0.027190           0.021082   \n",
       "Road_Surface_Conditions     -0.067232  0.085564           0.013631   \n",
       "Special_Conditions_at_Site  -0.015001  0.014238          -0.001281   \n",
       "Urban_or_Rural_Area         -0.100809  0.058659          -0.083930   \n",
       "\n",
       "                            Number_of_Vehicles  Number_of_Casualties  \\\n",
       "Longitude                             0.015126             -0.040334   \n",
       "Latitude                             -0.038574              0.029588   \n",
       "Accident_Severity                     0.076186             -0.068747   \n",
       "Number_of_Vehicles                    1.000000              0.245430   \n",
       "Number_of_Casualties                  0.245430              1.000000   \n",
       "Day_of_Week                           0.002406              0.000895   \n",
       "Local_Authority_(District)           -0.006458              0.016901   \n",
       "Speed_limit                           0.082694              0.139023   \n",
       "Light_Conditions                     -0.082485              0.030567   \n",
       "Weather_Conditions                   -0.023843             -0.002428   \n",
       "Road_Surface_Conditions              -0.050638              0.022130   \n",
       "Special_Conditions_at_Site           -0.014334              0.010883   \n",
       "Urban_or_Rural_Area                   0.039475              0.113402   \n",
       "\n",
       "                            Day_of_Week  Local_Authority_(District)  \\\n",
       "Longitude                     -0.000378                   -0.404787   \n",
       "Latitude                       0.003977                    0.141626   \n",
       "Accident_Severity              0.003336                   -0.044134   \n",
       "Number_of_Vehicles             0.002406                   -0.006458   \n",
       "Number_of_Casualties           0.000895                    0.016901   \n",
       "Day_of_Week                    1.000000                   -0.000881   \n",
       "Local_Authority_(District)    -0.000881                    1.000000   \n",
       "Speed_limit                   -0.016014                    0.205138   \n",
       "Light_Conditions               0.007572                    0.011777   \n",
       "Weather_Conditions            -0.001683                    0.048413   \n",
       "Road_Surface_Conditions       -0.009044                    0.081917   \n",
       "Special_Conditions_at_Site    -0.002755                    0.025646   \n",
       "Urban_or_Rural_Area           -0.017346                    0.257345   \n",
       "\n",
       "                            Speed_limit  Light_Conditions  Weather_Conditions  \\\n",
       "Longitude                     -0.061076          0.008590           -0.027861   \n",
       "Latitude                       0.062134          0.002957            0.027190   \n",
       "Accident_Severity             -0.079701         -0.054637            0.021082   \n",
       "Number_of_Vehicles             0.082694         -0.082485           -0.023843   \n",
       "Number_of_Casualties           0.139023          0.030567           -0.002428   \n",
       "Day_of_Week                   -0.016014          0.007572           -0.001683   \n",
       "Local_Authority_(District)     0.205138          0.011777            0.048413   \n",
       "Speed_limit                    1.000000          0.093459            0.036517   \n",
       "Light_Conditions               0.093459          1.000000            0.115586   \n",
       "Weather_Conditions             0.036517          0.115586            1.000000   \n",
       "Road_Surface_Conditions        0.132536          0.191809            0.315677   \n",
       "Special_Conditions_at_Site     0.071674          0.005631            0.018757   \n",
       "Urban_or_Rural_Area            0.682524          0.072806            0.039091   \n",
       "\n",
       "                            Road_Surface_Conditions  \\\n",
       "Longitude                                 -0.067232   \n",
       "Latitude                                   0.085564   \n",
       "Accident_Severity                          0.013631   \n",
       "Number_of_Vehicles                        -0.050638   \n",
       "Number_of_Casualties                       0.022130   \n",
       "Day_of_Week                               -0.009044   \n",
       "Local_Authority_(District)                 0.081917   \n",
       "Speed_limit                                0.132536   \n",
       "Light_Conditions                           0.191809   \n",
       "Weather_Conditions                         0.315677   \n",
       "Road_Surface_Conditions                    1.000000   \n",
       "Special_Conditions_at_Site                 0.046739   \n",
       "Urban_or_Rural_Area                        0.134827   \n",
       "\n",
       "                            Special_Conditions_at_Site  Urban_or_Rural_Area  \n",
       "Longitude                                    -0.015001            -0.100809  \n",
       "Latitude                                      0.014238             0.058659  \n",
       "Accident_Severity                            -0.001281            -0.083930  \n",
       "Number_of_Vehicles                           -0.014334             0.039475  \n",
       "Number_of_Casualties                          0.010883             0.113402  \n",
       "Day_of_Week                                  -0.002755            -0.017346  \n",
       "Local_Authority_(District)                    0.025646             0.257345  \n",
       "Speed_limit                                   0.071674             0.682524  \n",
       "Light_Conditions                              0.005631             0.072806  \n",
       "Weather_Conditions                            0.018757             0.039091  \n",
       "Road_Surface_Conditions                       0.046739             0.134827  \n",
       "Special_Conditions_at_Site                    1.000000             0.073730  \n",
       "Urban_or_Rural_Area                           0.073730             1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_cols1 = df_accidents.select_dtypes(include=[np.number])\n",
    "corr_matrix1 = numerical_cols1.corr()\n",
    "corr_matrix1 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>Time</th>\n",
       "      <th>Local_Authority_(District)</th>\n",
       "      <th>Local_Authority_(Highway)</th>\n",
       "      <th>Speed_limit</th>\n",
       "      <th>Light_Conditions</th>\n",
       "      <th>Weather_Conditions</th>\n",
       "      <th>Road_Surface_Conditions</th>\n",
       "      <th>Special_Conditions_at_Site</th>\n",
       "      <th>Urban_or_Rural_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200901BS70001</td>\n",
       "      <td>-0.201349</td>\n",
       "      <td>51.512273</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>15:11:00</td>\n",
       "      <td>12</td>\n",
       "      <td>E09000020</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>-0.199248</td>\n",
       "      <td>51.514399</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2009-01-05</td>\n",
       "      <td>2</td>\n",
       "      <td>10:59:00</td>\n",
       "      <td>12</td>\n",
       "      <td>E09000020</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200901BS70003</td>\n",
       "      <td>-0.179599</td>\n",
       "      <td>51.486668</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>14:19:00</td>\n",
       "      <td>12</td>\n",
       "      <td>E09000020</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200901BS70004</td>\n",
       "      <td>-0.203110</td>\n",
       "      <td>51.507804</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-01-05</td>\n",
       "      <td>2</td>\n",
       "      <td>08:10:00</td>\n",
       "      <td>12</td>\n",
       "      <td>E09000020</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200901BS70005</td>\n",
       "      <td>-0.173445</td>\n",
       "      <td>51.482076</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-01-06</td>\n",
       "      <td>3</td>\n",
       "      <td>17:25:00</td>\n",
       "      <td>12</td>\n",
       "      <td>E09000020</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident_Index  Longitude   Latitude  Accident_Severity  Number_of_Vehicles  \\\n",
       "0  200901BS70001  -0.201349  51.512273                  2                   2   \n",
       "1  200901BS70002  -0.199248  51.514399                  2                   2   \n",
       "2  200901BS70003  -0.179599  51.486668                  3                   2   \n",
       "3  200901BS70004  -0.203110  51.507804                  2                   2   \n",
       "4  200901BS70005  -0.173445  51.482076                  2                   2   \n",
       "\n",
       "   Number_of_Casualties        Date  Day_of_Week      Time  \\\n",
       "0                     1  2009-01-01            5  15:11:00   \n",
       "1                    11  2009-01-05            2  10:59:00   \n",
       "2                     1  2009-01-04            1  14:19:00   \n",
       "3                     1  2009-01-05            2  08:10:00   \n",
       "4                     1  2009-01-06            3  17:25:00   \n",
       "\n",
       "   Local_Authority_(District) Local_Authority_(Highway)  Speed_limit  \\\n",
       "0                          12                 E09000020           30   \n",
       "1                          12                 E09000020           30   \n",
       "2                          12                 E09000020           30   \n",
       "3                          12                 E09000020           30   \n",
       "4                          12                 E09000020           30   \n",
       "\n",
       "   Light_Conditions  Weather_Conditions  Road_Surface_Conditions  \\\n",
       "0                 1                   1                        1   \n",
       "1                 1                   1                        2   \n",
       "2                 1                   1                        1   \n",
       "3                 1                   8                        4   \n",
       "4                 4                   1                        1   \n",
       "\n",
       "   Special_Conditions_at_Site  Urban_or_Rural_Area  \n",
       "0                           0                    1  \n",
       "1                           0                    1  \n",
       "2                           0                    1  \n",
       "3                           0                    1  \n",
       "4                           0                    1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accidents.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick view on Vehicles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Accident_Index                  0\n",
       "Vehicle_Type                    0\n",
       "Was_Vehicle_Left_Hand_Drive?    0\n",
       "Journey_Purpose_of_Driver       0\n",
       "Sex_of_Driver                   0\n",
       "Age_of_Driver                   0\n",
       "Age_of_Vehicle                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vechiles.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3113\n"
     ]
    }
   ],
   "source": [
    "print(df_vechiles.duplicated().sum())\n",
    "# important note that is not really duplicated , it just because the removed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1640704 entries, 0 to 1640703\n",
      "Data columns (total 7 columns):\n",
      " #   Column                        Non-Null Count    Dtype \n",
      "---  ------                        --------------    ----- \n",
      " 0   Accident_Index                1640704 non-null  object\n",
      " 1   Vehicle_Type                  1640704 non-null  int64 \n",
      " 2   Was_Vehicle_Left_Hand_Drive?  1640704 non-null  int64 \n",
      " 3   Journey_Purpose_of_Driver     1640704 non-null  int64 \n",
      " 4   Sex_of_Driver                 1640704 non-null  int64 \n",
      " 5   Age_of_Driver                 1640704 non-null  int64 \n",
      " 6   Age_of_Vehicle                1640704 non-null  int64 \n",
      "dtypes: int64(6), object(1)\n",
      "memory usage: 87.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_vechiles.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1640704, 7)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vechiles.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Vehicle_Type</th>\n",
       "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
       "      <th>Journey_Purpose_of_Driver</th>\n",
       "      <th>Sex_of_Driver</th>\n",
       "      <th>Age_of_Driver</th>\n",
       "      <th>Age_of_Vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1640704</td>\n",
       "      <td>1.640704e+06</td>\n",
       "      <td>1.640704e+06</td>\n",
       "      <td>1.640704e+06</td>\n",
       "      <td>1.640704e+06</td>\n",
       "      <td>1.640704e+06</td>\n",
       "      <td>1.640704e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>898471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2013460234852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.551219e+00</td>\n",
       "      <td>1.004265e+00</td>\n",
       "      <td>7.035593e+00</td>\n",
       "      <td>1.405757e+00</td>\n",
       "      <td>3.927871e+01</td>\n",
       "      <td>7.458439e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.388348e+00</td>\n",
       "      <td>6.516478e-02</td>\n",
       "      <td>5.052299e+00</td>\n",
       "      <td>5.992807e-01</td>\n",
       "      <td>1.515689e+01</td>\n",
       "      <td>3.945962e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.800000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.800000e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.110000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Accident_Index  Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  \\\n",
       "count         1640704  1.640704e+06                  1.640704e+06   \n",
       "unique         898471           NaN                           NaN   \n",
       "top     2013460234852           NaN                           NaN   \n",
       "freq               67           NaN                           NaN   \n",
       "mean              NaN  9.551219e+00                  1.004265e+00   \n",
       "std               NaN  8.388348e+00                  6.516478e-02   \n",
       "min               NaN  1.000000e+00                  1.000000e+00   \n",
       "25%               NaN  9.000000e+00                  1.000000e+00   \n",
       "50%               NaN  9.000000e+00                  1.000000e+00   \n",
       "75%               NaN  9.000000e+00                  1.000000e+00   \n",
       "max               NaN  9.800000e+01                  2.000000e+00   \n",
       "\n",
       "        Journey_Purpose_of_Driver  Sex_of_Driver  Age_of_Driver  \\\n",
       "count                1.640704e+06   1.640704e+06   1.640704e+06   \n",
       "unique                        NaN            NaN            NaN   \n",
       "top                           NaN            NaN            NaN   \n",
       "freq                          NaN            NaN            NaN   \n",
       "mean                 7.035593e+00   1.405757e+00   3.927871e+01   \n",
       "std                  5.052299e+00   5.992807e-01   1.515689e+01   \n",
       "min                  1.000000e+00   1.000000e+00   1.000000e+00   \n",
       "25%                  2.000000e+00   1.000000e+00   2.800000e+01   \n",
       "50%                  6.000000e+00   1.000000e+00   3.800000e+01   \n",
       "75%                  1.500000e+01   2.000000e+00   4.800000e+01   \n",
       "max                  1.500000e+01   3.000000e+00   1.000000e+02   \n",
       "\n",
       "        Age_of_Vehicle  \n",
       "count     1.640704e+06  \n",
       "unique             NaN  \n",
       "top                NaN  \n",
       "freq               NaN  \n",
       "mean      7.458439e+00  \n",
       "std       3.945962e+00  \n",
       "min       1.000000e+00  \n",
       "25%       5.000000e+00  \n",
       "50%       7.000000e+00  \n",
       "75%       9.000000e+00  \n",
       "max       1.110000e+02  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vechiles.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vehicle_Type</th>\n",
       "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
       "      <th>Journey_Purpose_of_Driver</th>\n",
       "      <th>Sex_of_Driver</th>\n",
       "      <th>Age_of_Driver</th>\n",
       "      <th>Age_of_Vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vehicle_Type</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055821</td>\n",
       "      <td>-0.085701</td>\n",
       "      <td>0.022858</td>\n",
       "      <td>0.077285</td>\n",
       "      <td>-0.032123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
       "      <td>0.055821</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.033804</td>\n",
       "      <td>-0.021180</td>\n",
       "      <td>0.004692</td>\n",
       "      <td>-0.004581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey_Purpose_of_Driver</th>\n",
       "      <td>-0.085701</td>\n",
       "      <td>-0.033804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.094867</td>\n",
       "      <td>-0.028688</td>\n",
       "      <td>0.037290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_of_Driver</th>\n",
       "      <td>0.022858</td>\n",
       "      <td>-0.021180</td>\n",
       "      <td>0.094867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029755</td>\n",
       "      <td>-0.017901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_of_Driver</th>\n",
       "      <td>0.077285</td>\n",
       "      <td>0.004692</td>\n",
       "      <td>-0.028688</td>\n",
       "      <td>-0.029755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_of_Vehicle</th>\n",
       "      <td>-0.032123</td>\n",
       "      <td>-0.004581</td>\n",
       "      <td>0.037290</td>\n",
       "      <td>-0.017901</td>\n",
       "      <td>-0.031737</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  \\\n",
       "Vehicle_Type                      1.000000                      0.055821   \n",
       "Was_Vehicle_Left_Hand_Drive?      0.055821                      1.000000   \n",
       "Journey_Purpose_of_Driver        -0.085701                     -0.033804   \n",
       "Sex_of_Driver                     0.022858                     -0.021180   \n",
       "Age_of_Driver                     0.077285                      0.004692   \n",
       "Age_of_Vehicle                   -0.032123                     -0.004581   \n",
       "\n",
       "                              Journey_Purpose_of_Driver  Sex_of_Driver  \\\n",
       "Vehicle_Type                                  -0.085701       0.022858   \n",
       "Was_Vehicle_Left_Hand_Drive?                  -0.033804      -0.021180   \n",
       "Journey_Purpose_of_Driver                      1.000000       0.094867   \n",
       "Sex_of_Driver                                  0.094867       1.000000   \n",
       "Age_of_Driver                                 -0.028688      -0.029755   \n",
       "Age_of_Vehicle                                 0.037290      -0.017901   \n",
       "\n",
       "                              Age_of_Driver  Age_of_Vehicle  \n",
       "Vehicle_Type                       0.077285       -0.032123  \n",
       "Was_Vehicle_Left_Hand_Drive?       0.004692       -0.004581  \n",
       "Journey_Purpose_of_Driver         -0.028688        0.037290  \n",
       "Sex_of_Driver                     -0.029755       -0.017901  \n",
       "Age_of_Driver                      1.000000       -0.031737  \n",
       "Age_of_Vehicle                    -0.031737        1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_cols2 = df_vechiles.select_dtypes(include=[np.number])\n",
    "corr_matrix2 = numerical_cols2.corr()\n",
    "corr_matrix2 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Vehicle_Type</th>\n",
       "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
       "      <th>Journey_Purpose_of_Driver</th>\n",
       "      <th>Sex_of_Driver</th>\n",
       "      <th>Age_of_Driver</th>\n",
       "      <th>Age_of_Vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200901BS70001</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200901BS70001</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200901BS70003</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident_Index  Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  \\\n",
       "0  200901BS70001             2                             1   \n",
       "1  200901BS70001             9                             1   \n",
       "2  200901BS70002            19                             1   \n",
       "3  200901BS70002            11                             1   \n",
       "4  200901BS70003             3                             1   \n",
       "\n",
       "   Journey_Purpose_of_Driver  Sex_of_Driver  Age_of_Driver  Age_of_Vehicle  \n",
       "0                         15              1             77              26  \n",
       "1                         15              1             31               2  \n",
       "2                          1              1             40               3  \n",
       "3                          1              1             38               1  \n",
       "4                         15              1             40               2  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vechiles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick view on Casualties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Accident_Index            0\n",
       "Casualty_Reference        0\n",
       "Sex_of_Casualty           0\n",
       "Age_of_Casualty           0\n",
       "Casualty_Severity         0\n",
       "Car_Passenger             0\n",
       "Bus_or_Coach_Passenger    0\n",
       "Casualty_Type             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casu.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df_casu.duplicated().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1185983 entries, 0 to 1185982\n",
      "Data columns (total 8 columns):\n",
      " #   Column                  Non-Null Count    Dtype \n",
      "---  ------                  --------------    ----- \n",
      " 0   Accident_Index          1185983 non-null  object\n",
      " 1   Casualty_Reference      1185983 non-null  int64 \n",
      " 2   Sex_of_Casualty         1185983 non-null  int64 \n",
      " 3   Age_of_Casualty         1185983 non-null  int64 \n",
      " 4   Casualty_Severity       1185983 non-null  int64 \n",
      " 5   Car_Passenger           1185983 non-null  int64 \n",
      " 6   Bus_or_Coach_Passenger  1185983 non-null  int64 \n",
      " 7   Casualty_Type           1185983 non-null  int64 \n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 72.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_casu.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1185983, 8)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casu.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Casualty_Reference</th>\n",
       "      <th>Sex_of_Casualty</th>\n",
       "      <th>Age_of_Casualty</th>\n",
       "      <th>Casualty_Severity</th>\n",
       "      <th>Car_Passenger</th>\n",
       "      <th>Bus_or_Coach_Passenger</th>\n",
       "      <th>Casualty_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1185983</td>\n",
       "      <td>1.185983e+06</td>\n",
       "      <td>1.185983e+06</td>\n",
       "      <td>1.185983e+06</td>\n",
       "      <td>1.185983e+06</td>\n",
       "      <td>1.185983e+06</td>\n",
       "      <td>1.185983e+06</td>\n",
       "      <td>1.185983e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>885429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>20144100J0489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.431425e+00</td>\n",
       "      <td>1.415368e+00</td>\n",
       "      <td>3.576307e+01</td>\n",
       "      <td>2.866305e+00</td>\n",
       "      <td>2.729634e-01</td>\n",
       "      <td>8.525839e-02</td>\n",
       "      <td>7.387409e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.608286e+00</td>\n",
       "      <td>4.927855e-01</td>\n",
       "      <td>1.850742e+01</td>\n",
       "      <td>3.670351e-01</td>\n",
       "      <td>5.857770e-01</td>\n",
       "      <td>5.507572e-01</td>\n",
       "      <td>7.306746e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.520000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.800000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Accident_Index  Casualty_Reference  Sex_of_Casualty  Age_of_Casualty  \\\n",
       "count         1185983        1.185983e+06     1.185983e+06     1.185983e+06   \n",
       "unique         885429                 NaN              NaN              NaN   \n",
       "top     20144100J0489                 NaN              NaN              NaN   \n",
       "freq               93                 NaN              NaN              NaN   \n",
       "mean              NaN        1.431425e+00     1.415368e+00     3.576307e+01   \n",
       "std               NaN        1.608286e+00     4.927855e-01     1.850742e+01   \n",
       "min               NaN        1.000000e+00     1.000000e+00     0.000000e+00   \n",
       "25%               NaN        1.000000e+00     1.000000e+00     2.100000e+01   \n",
       "50%               NaN        1.000000e+00     1.000000e+00     3.200000e+01   \n",
       "75%               NaN        2.000000e+00     2.000000e+00     4.800000e+01   \n",
       "max               NaN        8.520000e+02     2.000000e+00     1.030000e+02   \n",
       "\n",
       "        Casualty_Severity  Car_Passenger  Bus_or_Coach_Passenger  \\\n",
       "count        1.185983e+06   1.185983e+06            1.185983e+06   \n",
       "unique                NaN            NaN                     NaN   \n",
       "top                   NaN            NaN                     NaN   \n",
       "freq                  NaN            NaN                     NaN   \n",
       "mean         2.866305e+00   2.729634e-01            8.525839e-02   \n",
       "std          3.670351e-01   5.857770e-01            5.507572e-01   \n",
       "min          1.000000e+00   0.000000e+00            0.000000e+00   \n",
       "25%          3.000000e+00   0.000000e+00            0.000000e+00   \n",
       "50%          3.000000e+00   0.000000e+00            0.000000e+00   \n",
       "75%          3.000000e+00   0.000000e+00            0.000000e+00   \n",
       "max          3.000000e+00   2.000000e+00            4.000000e+00   \n",
       "\n",
       "        Casualty_Type  \n",
       "count    1.185983e+06  \n",
       "unique            NaN  \n",
       "top               NaN  \n",
       "freq              NaN  \n",
       "mean     7.387409e+00  \n",
       "std      7.306746e+00  \n",
       "min      0.000000e+00  \n",
       "25%      3.000000e+00  \n",
       "50%      9.000000e+00  \n",
       "75%      9.000000e+00  \n",
       "max      9.800000e+01  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casu.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Casualty_Reference</th>\n",
       "      <th>Sex_of_Casualty</th>\n",
       "      <th>Age_of_Casualty</th>\n",
       "      <th>Casualty_Severity</th>\n",
       "      <th>Car_Passenger</th>\n",
       "      <th>Bus_or_Coach_Passenger</th>\n",
       "      <th>Casualty_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Casualty_Reference</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045747</td>\n",
       "      <td>-0.052091</td>\n",
       "      <td>0.028655</td>\n",
       "      <td>0.254979</td>\n",
       "      <td>0.171161</td>\n",
       "      <td>0.082904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_of_Casualty</th>\n",
       "      <td>0.045747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042274</td>\n",
       "      <td>0.084345</td>\n",
       "      <td>0.154851</td>\n",
       "      <td>0.083282</td>\n",
       "      <td>0.044880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_of_Casualty</th>\n",
       "      <td>-0.052091</td>\n",
       "      <td>0.042274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.058061</td>\n",
       "      <td>-0.177716</td>\n",
       "      <td>0.075631</td>\n",
       "      <td>0.088107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casualty_Severity</th>\n",
       "      <td>0.028655</td>\n",
       "      <td>0.084345</td>\n",
       "      <td>-0.058061</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061529</td>\n",
       "      <td>0.030193</td>\n",
       "      <td>0.081022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car_Passenger</th>\n",
       "      <td>0.254979</td>\n",
       "      <td>0.154851</td>\n",
       "      <td>-0.177716</td>\n",
       "      <td>0.061529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063550</td>\n",
       "      <td>0.120632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus_or_Coach_Passenger</th>\n",
       "      <td>0.171161</td>\n",
       "      <td>0.083282</td>\n",
       "      <td>0.075631</td>\n",
       "      <td>0.030193</td>\n",
       "      <td>-0.063550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casualty_Type</th>\n",
       "      <td>0.082904</td>\n",
       "      <td>0.044880</td>\n",
       "      <td>0.088107</td>\n",
       "      <td>0.081022</td>\n",
       "      <td>0.120632</td>\n",
       "      <td>0.076990</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Casualty_Reference  Sex_of_Casualty  Age_of_Casualty  \\\n",
       "Casualty_Reference                1.000000         0.045747        -0.052091   \n",
       "Sex_of_Casualty                   0.045747         1.000000         0.042274   \n",
       "Age_of_Casualty                  -0.052091         0.042274         1.000000   \n",
       "Casualty_Severity                 0.028655         0.084345        -0.058061   \n",
       "Car_Passenger                     0.254979         0.154851        -0.177716   \n",
       "Bus_or_Coach_Passenger            0.171161         0.083282         0.075631   \n",
       "Casualty_Type                     0.082904         0.044880         0.088107   \n",
       "\n",
       "                        Casualty_Severity  Car_Passenger  \\\n",
       "Casualty_Reference               0.028655       0.254979   \n",
       "Sex_of_Casualty                  0.084345       0.154851   \n",
       "Age_of_Casualty                 -0.058061      -0.177716   \n",
       "Casualty_Severity                1.000000       0.061529   \n",
       "Car_Passenger                    0.061529       1.000000   \n",
       "Bus_or_Coach_Passenger           0.030193      -0.063550   \n",
       "Casualty_Type                    0.081022       0.120632   \n",
       "\n",
       "                        Bus_or_Coach_Passenger  Casualty_Type  \n",
       "Casualty_Reference                    0.171161       0.082904  \n",
       "Sex_of_Casualty                       0.083282       0.044880  \n",
       "Age_of_Casualty                       0.075631       0.088107  \n",
       "Casualty_Severity                     0.030193       0.081022  \n",
       "Car_Passenger                        -0.063550       0.120632  \n",
       "Bus_or_Coach_Passenger                1.000000       0.076990  \n",
       "Casualty_Type                         0.076990       1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_cols3 = df_casu.select_dtypes(include=[np.number])\n",
    "corr_matrix3 = numerical_cols3.corr()\n",
    "corr_matrix3 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Casualty_Reference</th>\n",
       "      <th>Sex_of_Casualty</th>\n",
       "      <th>Age_of_Casualty</th>\n",
       "      <th>Casualty_Severity</th>\n",
       "      <th>Car_Passenger</th>\n",
       "      <th>Bus_or_Coach_Passenger</th>\n",
       "      <th>Casualty_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200901BS70001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200901BS70002</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident_Index  Casualty_Reference  Sex_of_Casualty  Age_of_Casualty  \\\n",
       "0  200901BS70001                   1                1               77   \n",
       "1  200901BS70002                   1                1               40   \n",
       "2  200901BS70002                   2                1               38   \n",
       "3  200901BS70002                   3                2               76   \n",
       "4  200901BS70002                   4                1               42   \n",
       "\n",
       "   Casualty_Severity  Car_Passenger  Bus_or_Coach_Passenger  Casualty_Type  \n",
       "0                  2              0                       0              2  \n",
       "1                  3              0                       0             19  \n",
       "2                  3              0                       0             11  \n",
       "3                  2              0                       4             11  \n",
       "4                  2              0                       4             11  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casu.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overall view "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accident_Severity\n",
      "3    763607\n",
      "2    124471\n",
      "1     10477\n",
      "Name: count, dtype: int64\n",
      "Vehicle_Type\n",
      "9     1186286\n",
      "1      116783\n",
      "19      78124\n",
      "5       46534\n",
      "3       43576\n",
      "11      40697\n",
      "8       30996\n",
      "21      30980\n",
      "2       18211\n",
      "4       14530\n",
      "90      13023\n",
      "20      11297\n",
      "10       4198\n",
      "17       3429\n",
      "16        761\n",
      "22        451\n",
      "98        387\n",
      "97        285\n",
      "18        137\n",
      "23         19\n",
      "Name: count, dtype: int64\n",
      "Casualty_Severity\n",
      "3    1038627\n",
      "2     136152\n",
      "1      11204\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_accidents['Accident_Severity'].value_counts())\n",
    "print(df_vechiles['Vehicle_Type'].value_counts())\n",
    "print(df_casu['Casualty_Severity'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 17)\n",
      "(0, 7)\n",
      "(0, 8)\n"
     ]
    }
   ],
   "source": [
    "print(df_accidents[df_accidents['Weather_Conditions'] == -1].shape)\n",
    "print(df_vechiles[df_vechiles['Vehicle_Type'] == -1].shape)\n",
    "print(df_casu[df_casu['Sex_of_Casualty'] == -1].shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}